Required R packages

The diprate package is available from GitHub here: dipDRC

library(diprate)

Static data

static <- read.csv("../data/from_CW_via_Slack_20210507/StaticDF.csv", row.names=1)
static <- static[order(static$Culture_Type,static$Cell_Line),]
static[static$Cell_Conc==0,"Cell_Conc"] <- 1
cell_lines <- unique(static$Cell_Line)

Outlier value in CORL279

static <- static[!(static$Cell_Line=="CORL279" & static$RLU < 10),]

Code for reproducing figures

Correlation between luminescence and cell count

par(mfrow=c(2,5))
linear_models <- lapply(cell_lines, function(cl) {
        dat <- static[static$Cell_Line==cl,]
        culture_type <- unique(dat$Culture_Type)
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data = dat)
        plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=c(0,14), ylim=c(7,18))
        abline(m, col="blue")
        return(m)
    })

names(linear_models) <- cell_lines

Need 2-part function to accommodate minimum values

Assuming first part of data is at some minimum value and not associated with any actual cell count (lower limit of detection), which would result in slope=0 (values are constant until some minimum number of cells is achieved).

lagLine <- function (x, lower, slope, br = 64) sapply(x, function(z) ifelse(z <= br, lower, lower + (z-br) * slope))

fitLagLin <- function(x, y, start_list=list(lower=5, slope=1, br=1)) 
    nls(y ~ lagLine(x=x, lower, slope, br),
        start = start_list,
        algorithm="port",
        control=nls.control(maxiter=500)
        )

Fit the lag-linear model to data

par(mfrow=c(2,5))
lag_linear_models <- lapply(cell_lines, function(cl) {
    m <- tryCatch({
        dat <- static[static$Cell_Line==cl,]
        culture_type <- unique(dat$Culture_Type)
        m <- fitLagLin(log2(dat$Cell_Conc), log2(dat$RLU))
    }, error=function(e) { return(e) })
    # if(culture_type == "Adherent") 
    # {
    #     xr <- c(4,12)
    # } else {
    #     xr <- c(6,14)
    # }
    xr <- c(0,14)
    plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=xr, ylim=c(7,18))
    if(class(m)[1] != "nls")
    {
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data=dat[dat$Cell_Conc>1,])
        abline(m, col="blue", lwd=2)
    } else {
        curve(from=0.5,to=18, lagLine(x, lower=coef(m)['lower'], 
                                      slope=coef(m)['slope'], 
                                      br=coef(m)['br']), 
              col="blue", lwd=2, add=TRUE)
    }
    # text(12, 9, paste("Adj R2 ="))
    return(m)
})

names(lag_linear_models) <- cell_lines

Try eliminating controls

Assume all luminescence values with cells produce detectable signal.

par(mfrow=c(2,5))
linear_models <- lapply(cell_lines, function(cl) {
        dat <- static[static$Cell_Line==cl & static$Cell_Conc >1,]
        culture_type <- unique(dat$Culture_Type)
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data = dat)
        plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=c(0,14), ylim=c(7,18))
        abline(m, col="blue")
        return(m)
    })

names(linear_models) <- cell_lines

Compare to linear models

Assuming the lowest number of cells is above the threshold of detection, will remove the no cells control and fit remaining data.

dat <- static[static$Cell_Conc > 1,]
m2 <- lme4::lmList(log2(RLU) ~ log2(Cell_Conc) | Cell_Line, data=dat)
f2 <- coef(m2)
r2 <- unlist(summary(m2)$adj.r.squared)
par(mfrow=c(2,5))
temp <- lapply(cell_lines, function(cl) {
    dtp <- dat[dat$Cell_Line==cl,]
    culture_type <- unique(dtp[dtp$Cell_Line==cl,'Culture_Type'])
    if(culture_type == "Adherent") 
    {
        xr <- c(5,12)
    } else {
        xr <- c(7,14)
    }
    plot(log2(RLU) ~ log2(Cell_Conc), 
         data=dtp, 
         main=paste0(cl," (",culture_type,")"), 
         xlim=xr, ylim=c(7,18))
    abline(m2[[cl]], col="blue", lwd=2)
    text(xr[1]+0.25, 17, pos=4, paste("slope =",signif(f2[cl,2],3)))
    text(xr[1]+0.25, 16, pos=4, expression(R^2))
    text(xr[1]+1, 16, pos=4, paste("=", signif(r2[cl],3)))
})

fitLagLin1 <- function(x, y, start_list=list(lower=5, br=1)) 
    nls(y ~ lagLine(x=x, lower, slope=1, br),
        start = start_list,
        algorithm="port",
        control=nls.control(maxiter=500)
        )

par(mfrow=c(2,5))
lag_linear1_models <- lapply(cell_lines, function(cl) {
    m <- tryCatch({
        dat <- static[static$Cell_Line==cl,]
        culture_type <- unique(dat$Culture_Type)
        m <- fitLagLin1(log2(dat$Cell_Conc), log2(dat$RLU))
    }, error=function(e) { return(e) })
    # if(culture_type == "Adherent") 
    # {
    #     xr <- c(4,12)
    # } else {
    #     xr <- c(6,14)
    # }
    xr <- c(0,14)
    plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=xr, ylim=c(7,18))
    if(class(m)[1] != "nls")
    {
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data=dat[dat$Cell_Conc>1,])
        abline(m, col="blue", lwd=2)
    } else {
        curve(from=0.5,to=18, lagLine(x, lower=coef(m)['lower'], 
                                      slope=1, 
                                      br=coef(m)['br']), 
              col="blue", lwd=2, add=TRUE)
    }
    # text(12, 9, paste("Adj R2 ="))
    return(m)
})

names(lag_linear1_models) <- cell_lines

Combined cell count & lum data

lcc_cell_lines <- unique(lcc$cell.line)
ctrls <- lapply(lcc_cell_lines, function(cl) lcc[lcc$cell.line==cl & lcc$drug1.conc==0,])
names(ctrls) <- lcc_cell_lines

Cell counts

par(mfrow=c(2,2))
invisible(lapply(names(ctrls), function(n) do.call(plotGC, 
        append(getGCargs(ctrls[[n]], dat.col=c("time","Cell_Count","uid")),list(main=n, leg=FALSE)))))

Luminscence

par(mfrow=c(2,2))
invisible(lapply(names(ctrls), function(n) do.call(plotGC, 
        append(getGCargs(ctrls[[n]], dat.col=c("time","RLU","uid")),list(main=n)))))

dms53 <- ctrls[['DMS53']]

par(mfrow=c(1,2))

invisible(do.call(plotGC, append(getGCargs(dms53),list(main="DMS53, cell count", leg=FALSE))))
invisible(do.call(plotGC, append(getGCargs(dms53, dat.col=c("time","RLU","uid")),list(main="DMS53, lum", leg=FALSE))))

h1048 <- ctrls[['H1048']]

par(mfrow=c(1,2))

invisible(do.call(plotGC, append(getGCargs(h1048, dat.col=c("time","Cell_Count","uid")),list(main="H1048, cell count", leg=FALSE))))
invisible(do.call(plotGC, append(getGCargs(h1048, dat.col=c("time","RLU","uid")),list(main="H1048, lum", leg=FALSE))))

Sum of all control cell counts at each time point

invisible(do.call(plotGC, append(getGCargs(h1048, dat.col=c("time","RLU","uid")),list(main="H1048, lum", leg=FALSE))))
lines(log2(cell.count)-log2(cell.count)[1] ~ time, data=h1048_sumc, lwd=3)

---
title: "Real-time luminescence enables estimation of drug-induced proliferation rates in adherent and suspension cell lines"
author: "Darren Tyson & Clayton Wandishin"
date: "05/09/2021"
output: html_notebook
---

## Required R packages
The `diprate` package is available from GitHub here: [dipDRC](https://www.github.com/Qulab-VU/dipDRC)
```{r Setup}
library(diprate)
```

## Static data
```{r Load data}
static <- read.csv("../data/from_CW_via_Slack_20210507/StaticDF.csv", row.names=1)
static <- static[order(static$Culture_Type,static$Cell_Line),]
static[static$Cell_Conc==0,"Cell_Conc"] <- 1
cell_lines <- unique(static$Cell_Line)
```
#### Outlier value in CORL279
```{r}
static <- static[!(static$Cell_Line=="CORL279" & static$RLU < 10),]
```


## Code for reproducing figures
### Correlation between luminescence and cell count
```{r Cell count & luminescence, fig.height=3, fig.width=7}
par(mfrow=c(2,5))
linear_models <- lapply(cell_lines, function(cl) {
        dat <- static[static$Cell_Line==cl,]
        culture_type <- unique(dat$Culture_Type)
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data = dat)
        plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=c(0,14), ylim=c(7,18))
        abline(m, col="blue")
        return(m)
    })
names(linear_models) <- cell_lines
```

#### Need 2-part function to accommodate minimum values
Assuming first part of data is at some minimum value and not associated with any actual cell count (lower limit of detection), which would result in `slope=0` (values are constant until some minimum number of cells is achieved).
```{r Lag-linear function}
lagLine <- function (x, lower, slope, br = 64) sapply(x, function(z) ifelse(z <= br, lower, lower + (z-br) * slope))

fitLagLin <- function(x, y, start_list=list(lower=5, slope=1, br=1)) 
    nls(y ~ lagLine(x=x, lower, slope, br),
        start = start_list,
        algorithm="port",
        control=nls.control(maxiter=500)
        )
```

#### Fit the lag-linear model to data
```{r Lag-linear model fits, fig.height=3, fig.width=7}
par(mfrow=c(2,5))
lag_linear_models <- lapply(cell_lines, function(cl) {
    m <- tryCatch({
        dat <- static[static$Cell_Line==cl,]
        culture_type <- unique(dat$Culture_Type)
        m <- fitLagLin(log2(dat$Cell_Conc), log2(dat$RLU))
    }, error=function(e) { return(e) })
    # if(culture_type == "Adherent") 
    # {
    #     xr <- c(4,12)
    # } else {
    #     xr <- c(6,14)
    # }
    xr <- c(0,14)
    plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=xr, ylim=c(7,18))
    if(class(m)[1] != "nls")
    {
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data=dat[dat$Cell_Conc>1,])
        abline(m, col="blue", lwd=2)
    } else {
        curve(from=0.5,to=18, lagLine(x, lower=coef(m)['lower'], 
                                      slope=coef(m)['slope'], 
                                      br=coef(m)['br']), 
              col="blue", lwd=2, add=TRUE)
    }
    # text(12, 9, paste("Adj R2 ="))
    return(m)
})
names(lag_linear_models) <- cell_lines
```


#### Try eliminating controls
Assume all luminescence values with cells produce detectable signal.
```{r Cell count & luminescence minus control, fig.height=3, fig.width=7}
par(mfrow=c(2,5))
linear_models <- lapply(cell_lines, function(cl) {
        dat <- static[static$Cell_Line==cl & static$Cell_Conc >1,]
        culture_type <- unique(dat$Culture_Type)
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data = dat)
        plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=c(0,14), ylim=c(7,18))
        abline(m, col="blue")
        return(m)
    })
names(linear_models) <- cell_lines
```



#### Compare to linear models
Assuming the lowest number of cells is above the threshold of detection, will remove the no cells control and fit remaining data.
```{r}
dat <- static[static$Cell_Conc > 1,]
m2 <- lme4::lmList(log2(RLU) ~ log2(Cell_Conc) | Cell_Line, data=dat)
f2 <- coef(m2)
r2 <- unlist(summary(m2)$adj.r.squared)
```

```{r Linear models, fig.height=3, fig.width=7}
par(mfrow=c(2,5))
temp <- lapply(cell_lines, function(cl) {
    dtp <- dat[dat$Cell_Line==cl,]
    culture_type <- unique(dtp[dtp$Cell_Line==cl,'Culture_Type'])
    if(culture_type == "Adherent") 
    {
        xr <- c(5,12)
    } else {
        xr <- c(7,14)
    }
    plot(log2(RLU) ~ log2(Cell_Conc), 
         data=dtp, 
         main=paste0(cl," (",culture_type,")"), 
         xlim=xr, ylim=c(7,18))
    abline(m2[[cl]], col="blue", lwd=2)
    text(xr[1]+0.25, 17, pos=4, paste("slope =",signif(f2[cl,2],3)))
    text(xr[1]+0.25, 16, pos=4, expression(R^2))
    text(xr[1]+1, 16, pos=4, paste("=", signif(r2[cl],3)))
})

```

```{r Lag-linear slope eq 1 model fits, fig.height=3, fig.width=7}
fitLagLin1 <- function(x, y, start_list=list(lower=5, br=1)) 
    nls(y ~ lagLine(x=x, lower, slope=1, br),
        start = start_list,
        algorithm="port",
        control=nls.control(maxiter=500)
        )

par(mfrow=c(2,5))
lag_linear1_models <- lapply(cell_lines, function(cl) {
    m <- tryCatch({
        dat <- static[static$Cell_Line==cl,]
        culture_type <- unique(dat$Culture_Type)
        m <- fitLagLin1(log2(dat$Cell_Conc), log2(dat$RLU))
    }, error=function(e) { return(e) })
    # if(culture_type == "Adherent") 
    # {
    #     xr <- c(4,12)
    # } else {
    #     xr <- c(6,14)
    # }
    xr <- c(0,14)
    plot(log2(RLU) ~ log2(Cell_Conc), data=dat, main=paste0(cl," (",culture_type,")"), xlim=xr, ylim=c(7,18))
    if(class(m)[1] != "nls")
    {
        m <- lm(log2(RLU) ~ log2(Cell_Conc), data=dat[dat$Cell_Conc>1,])
        abline(m, col="blue", lwd=2)
    } else {
        curve(from=0.5,to=18, lagLine(x, lower=coef(m)['lower'], 
                                      slope=1, 
                                      br=coef(m)['br']), 
              col="blue", lwd=2, add=TRUE)
    }
    # text(12, 9, paste("Adj R2 ="))
    return(m)
})
names(lag_linear1_models) <- cell_lines
```



## Combined cell count & lum data

```{r}
lcc <- read.csv('../data/from_CW_via_Slack_20210507/20201216_Lum_CellCounts_Thunor.csv', row.names=1)
lcc <- lcc[,-1]
lcc$uid <- paste(lcc$upid,lcc$well,sep="_")
lcc <- lcc[order(lcc$uid,lcc$time),]
lcc <- lcc[lcc$time <= 96,]
```


```{r}
lcc_cell_lines <- unique(lcc$cell.line)
ctrls <- lapply(lcc_cell_lines, function(cl) lcc[lcc$cell.line==cl & lcc$drug1.conc==0,])
names(ctrls) <- lcc_cell_lines
```

#### Cell counts
```{r fig.height=6, fig.width=6}
par(mfrow=c(2,2))
invisible(lapply(names(ctrls), function(n) do.call(plotGC, 
        append(getGCargs(ctrls[[n]], dat.col=c("time","Cell_Count","uid")),list(main=n, leg=FALSE)))))
```
#### Luminscence
```{r Luminescence, fig.height=6, fig.width=6}
par(mfrow=c(2,2))
invisible(lapply(names(ctrls), function(n) do.call(plotGC, 
        append(getGCargs(ctrls[[n]], dat.col=c("time","RLU","uid")),list(main=n, leg=FALSE)))))
```

```{r DMS53, fig.height=3, fig.width=6}
dms53 <- ctrls[['DMS53']]

par(mfrow=c(1,2))

invisible(do.call(plotGC, append(getGCargs(dms53),list(main="DMS53, cell count", leg=FALSE))))
invisible(do.call(plotGC, append(getGCargs(dms53, dat.col=c("time","RLU","uid")),list(main="DMS53, lum", leg=FALSE))))
```

```{r H1048, fig.height=3, fig.width=6}
h1048 <- ctrls[['H1048']]

par(mfrow=c(1,2))

invisible(do.call(plotGC, append(getGCargs(h1048, dat.col=c("time","Cell_Count","uid")),list(main="H1048, cell count", leg=FALSE))))
invisible(do.call(plotGC, append(getGCargs(h1048, dat.col=c("time","RLU","uid")),list(main="H1048, lum", leg=FALSE))))
```
#### Sum of all control cell counts at each time point
```{r}
h1048_sumc <- sapply(unique(h1048$time), function(i) sum(h1048[h1048$time==i,"Cell_Count"]))
h1048_sumc <- data.frame(time=unique(h1048$time), cell.count=h1048_sumc)
plot(log2(cell.count)-log2(cell.count)[1] ~ time, data=h1048_sumc, type="l", ylab="Population doublings")
```

```{r}
invisible(do.call(plotGC, append(getGCargs(h1048, dat.col=c("time","RLU","uid")),list(main="H1048, lum", leg=FALSE))))
lines(log2(cell.count)-log2(cell.count)[1] ~ time, data=h1048_sumc, lwd=3)
```

